TNFSF8 Human

What is TNFSF8 and what is its role in human immune function?

TNFSF8, also known as CD30 Ligand, is a member of the tumor necrosis factor superfamily that plays significant roles in immune regulation. It functions primarily through binding to CD30 receptor (TNFRSF8), triggering signaling cascades that influence cell survival, proliferation, and cytokine production . In experimental studies, TNFSF8 has been shown to induce IL-6 secretion in human Hodgkin's lymphoma cell lines, demonstrating its role in inflammatory responses .

Methodologically, researchers studying TNFSF8 function typically employ flow cytometry for detection in human PBMCs (peripheral blood mononuclear cells), especially after stimulation with agents like PMA and Calcium Ionomycin . Functional studies often utilize neutralizing antibodies to block TNFSF8 activity in controlled cellular environments.

How is TNFSF8 expression detected in research settings?

TNFSF8 expression can be detected through multiple methodological approaches:

• Flow cytometry: Human PBMCs can be treated with PMA (50 ng/mL) and Calcium Ionomycin (200 ng/mL) overnight to induce expression, then stained with specific anti-TNFSF8 monoclonal antibodies .

• mRNA expression analysis: Transcriptomic approaches like RNA-seq or microarray analysis are commonly used in larger studies, as evidenced by TCGA breast cancer data analysis .

• Functional assays: TNFSF8 activity can be measured through its ability to induce IL-6 secretion in specific cell lines like HDLM human Hodgkin's lymphoma cells .

For reliable detection, cross-validation with multiple methods is recommended, particularly when studying different tissue types or disease states.

What is the association between TNFSF8 polymorphisms and neutrophil counts in cardiovascular disease?

Research has identified significant associations between specific TNFSF8 polymorphisms and neutrophil counts in post-myocardial infarction (MI) patients. The polymorphisms rs927374 (P=5.1 x 10⁻⁵) and rs2295800 (P=1.3 x 10⁻⁴) show strong associations with neutrophil counts . These single-nucleotide polymorphisms (SNPs) are in high linkage disequilibrium (r²=0.97), suggesting they may represent the same functional effect .

For rs927374, the data shows a clear genotype-dependent effect on neutrophil counts:

This association persisted after controlling for various clinical characteristics and remained unchanged after adjusting for case-control status . These findings suggest that genetic variation in TNFSF8 may influence the post-MI inflammatory response, offering potential insights into personalized cardiovascular disease management.

Methodologically, researchers investigating such associations should consider:

• Genotyping multiple SNPs in the TNFSF8 region

• Controlling for relevant clinical variables

• Performing linkage disequilibrium analysis to identify functional variants

• Validating findings in independent cohorts

How does TNFSF8 expression correlate with survival outcomes in breast cancer?

When analyzed by breast cancer subtype, TNFSF8 expression shows a significant correlation with OS in multiple subtypes:

• Luminal A: Higher expression correlates with better OS

• Luminal B: Higher expression correlates with better OS

• Basal-Like: Higher expression correlates with better OS

These findings suggest TNFSF8 may serve as a prognostic biomarker across different breast cancer subtypes .

For methodology in survival analysis studies:

• Use Kaplan-Meier analysis with appropriate statistical testing

• Stratify by molecular subtypes (Luminal A, Luminal B, HER2+, Basal-Like)

• Validate findings using independent cohorts

• Consider multivariate analysis to account for clinical covariates

How does TNFSF8 relate to immune cell infiltration in breast cancer?

Analysis using the TIMER database indicates that TNFSF8 expression positively correlates with infiltration of multiple immune cell types in breast cancer, including B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells (all with P<0.05) . This finding suggests TNFSF8 may play a role in modulating the tumor immune microenvironment.

Researchers investigating this relationship should:

• Use computational deconvolution methods to estimate immune cell infiltration from bulk RNA-seq data

• Validate computational findings with immunohistochemistry or flow cytometry

• Perform correlation analyses between gene expression and immune cell markers

• Consider single-cell RNA-seq to further resolve immune cell populations and their relationship with TNFSF8

What are the most effective methods for studying TNFSF8 function in vitro?

Several methodological approaches have proven effective for studying TNFSF8 function:

• Neutralization assays: The standard approach involves measuring IL-6 secretion induced by recombinant human TNFSF8 in HDLM human Hodgkin's lymphoma cells, using a cross-linking antibody (e.g., Mouse polyHistidine Monoclonal Antibody at 10 μg/mL) . Neutralization is then assessed by adding increasing concentrations of anti-TNFSF8 antibody.

• Flow cytometry: For detecting TNFSF8 expression on cell surfaces, researchers typically use anti-TNFSF8 monoclonal antibodies on stimulated cells (often using PMA and Calcium Ionomycin) .

• Gene expression manipulation: Knockdown or overexpression studies using siRNA, CRISPR-Cas9, or overexpression vectors can help determine TNFSF8's functional impact.

The optimal concentration for neutralization experiments (ND₅₀) is typically 1-4 μg/mL of anti-TNFSF8 antibody in the presence of 1 μg/mL recombinant human TNFSF8 . Researchers should always include appropriate controls, including isotype controls for antibody experiments.

How can researchers effectively analyze TNFSF8 genetic variants in population studies?

When investigating TNFSF8 genetic variants in population studies, researchers should consider:

• SNP selection strategy:

  • Focus on functional variants with potential regulatory effects

  • Include tag SNPs that capture linkage disequilibrium blocks

  • Consider SNPs with known associations (e.g., rs927374 and rs2295800)

• Sample size considerations:

  • Power calculations should account for expected effect sizes

  • For associations similar to the neutrophil count effect (16% difference between genotypes), substantial sample sizes are needed

• Statistical analysis approach:

  • Control for population stratification using principal component analysis

  • Adjust for relevant clinical covariates

  • Consider multiple testing correction (e.g., Bonferroni or False Discovery Rate)

  • Validate findings in independent cohorts

• Functional validation:

  • Follow-up significant associations with in vitro studies

  • Use reporter assays to assess regulatory effects of variants

  • Consider eQTL analysis to link variants to expression differences

How do we reconcile contradictory findings about TNFSF8 in different cancer studies?

When faced with contradictory findings regarding TNFSF8 across different cancer studies, researchers should consider several methodological factors:

• Cancer type specificity: TNFSF8's role may differ fundamentally between cancer types. In breast cancer, high expression correlates with better survival outcomes , but this pattern may not hold for other malignancies.

• Subtype heterogeneity: Even within a single cancer type, molecular subtypes can show different relationships with TNFSF8. Analysis of breast cancer subtypes shows that higher TNFSF8 expression correlates with better OS in Luminal A, Luminal B, and Basal-Like subtypes .

• Analysis methodology differences: Varying statistical approaches, cutoff definitions for "high" versus "low" expression, and adjustment for covariates can lead to apparently contradictory results.

• Context-dependent function: TNFSF8 may have different or even opposing effects depending on the immune microenvironment, which itself varies across cancer types and stages.

To reconcile contradictions, researchers should:

• Perform meta-analyses with careful attention to methodological differences

• Stratify analyses by cancer type, subtype, and stage

• Consider interaction effects with other immune markers

• Validate findings using multiple independent datasets

What is the significance of TNFSF8's correlation with immune cell infiltration?

The positive correlation between TNFSF8 expression and immune cell infiltration in breast cancer has several important implications:

Researchers should note that correlation does not imply causation, and functional studies are needed to determine whether TNFSF8 directly influences immune cell recruitment or whether its expression is simply a marker of an immunologically "hot" tumor microenvironment.

What are the best practices for detecting TNFSF8 protein expression in clinical samples?

For optimal detection of TNFSF8 protein in clinical samples, researchers should consider:

• Sample preparation:

  • For fresh tissue: Immediate processing or snap-freezing to preserve protein integrity

  • For blood: Isolation of PBMCs using density gradient centrifugation

  • For stimulated expression: Treatment with PMA (50 ng/mL) and Calcium Ionomycin (200 ng/mL) for blood cells

• Detection methods:

  • Flow cytometry: Using validated anti-TNFSF8 antibodies (e.g., Mouse Anti-Human CD30 Ligand/TNFSF8 Monoclonal Antibody)

  • Immunohistochemistry: With appropriate positive and negative controls

  • Western blotting: Using reduced protein loading for increased sensitivity

• Controls and validation:

  • Include isotype controls to assess non-specific binding

  • Use positive control samples with known TNFSF8 expression

  • Consider dual staining approaches to verify cell type-specific expression

• Quantification approaches:

  • For flow cytometry: Report mean fluorescence intensity ratios

  • For IHC: Use standardized scoring systems (H-score, Allred score)

  • Consider digital pathology for more objective quantification

How can researchers effectively study the functional impact of TNFSF8 polymorphisms?

To investigate the functional consequences of TNFSF8 polymorphisms such as rs927374 and rs2295800 , researchers should employ a multi-faceted approach:

• Expression quantitative trait loci (eQTL) analysis:

  • Assess whether the polymorphisms affect TNFSF8 mRNA or protein expression levels

  • Use available databases (GTEx, eQTLGen) to explore existing associations

  • Perform tissue-specific eQTL analysis when possible

• In vitro functional assays:

  • Luciferase reporter assays to assess effects on transcriptional regulation

  • CRISPR-based genome editing to introduce or correct specific variants

  • Neutrophil function assays to connect with clinical phenotypes

• Protein function studies:

  • Assess whether variants affect protein-protein interactions

  • Measure downstream signaling pathway activation

  • Evaluate cellular responses like cytokine production or proliferation

• In silico prediction:

  • Use computational tools to predict functional effects of coding variants

  • Analyze potential effects on transcription factor binding for regulatory variants

  • Evaluate evolutionary conservation at variant positions

What are the most promising research directions for TNFSF8 in human disease?

Based on current knowledge, several promising research directions for TNFSF8 include:

• Precision medicine applications:

  • Developing TNFSF8 genetic testing for personalized cardiovascular risk assessment

  • Exploring TNFSF8 expression as a prognostic biomarker in breast cancer

  • Investigating therapeutic targeting of TNFSF8 in inflammatory conditions

• Mechanistic studies:

  • Elucidating the exact mechanisms by which TNFSF8 influences neutrophil counts

  • Investigating how TNFSF8 impacts tumor immune microenvironment

  • Exploring the signaling pathways downstream of TNFSF8 in different cell types

• Clinical correlations:

  • Expanding association studies to diverse populations

  • Investigating TNFSF8's role in additional disease states

  • Determining whether TNFSF8 can predict response to immunotherapies

• Technical innovations:

  • Developing improved detection methods for TNFSF8 protein and genetic variants

  • Creating animal models with human-relevant TNFSF8 mutations

  • Exploring single-cell approaches to better understand cell type-specific effects